In hydrocarbon system evaluations, mapping faults and fault networks is essential to determine the migration pathways from the source to the reservoir. Faults can also help trap hydrocarbons or fragment a reservoir and therefore cause complications during field production.
Fault interpretation and fault network interpretation in three-dimensional seismic data can be facilitated and accelerated by the use of seismic amplitude discontinuity data. For example, U.S. patent application Ser. No. 09/827,574 (Cheng et al.) discloses a method of identifying structural and stratigraphic discontinuities in a three-dimensional seismic data volume through the use of seismic amplitude discontinuity data. In Cheng et al, the continuity of seismic reflectors in a volume of seismic amplitude data is measured by computing the correlation coefficient between adjacent seismic traces over a movable vertical window. A low coefficient of correlation indicates that the reflector is discontinuous. Repeating this practice over an entire volume of seismic data creates a discontinuity cube characterizing the continuity of the reflectors in the seismic volume. Since faults are detected by looking at vertical offsets of seismic reflectors, the discontinuity cube is a preferred way to image faults and fault networks in a volume of seismic data.
Other methods for detecting faults include converting seismic data volumes into cubes of coherency measurements herein referred to as “seismic coherency cubes.” This method is disclosed by U.S. Pat. Nos. 5,563,949 and 5,838,564 (Bahorich and Farmer), which are commonly known as the “coherency cube” patents. For purposes of this application, seismic coherency cube data and seismic discontinuity cube data can be used interchangeably as fault-indicating parameters in the inventive method claimed in this application, both as a substitute for each other or in combination.
Current technology in fault interpretation focuses on automatic fault detection and extraction using amplitude and coherency data. One characteristic of automatic fault detection is that no preexisting fault interpretations are required for automatic fault detection. However, a key issue in automatic fault detection is the quality of the seismic amplitude and coherency data used to detect the faults. Therefore, automatic fault detection methods require preprocessing of seismic data to enhance the quality of the fault signature in amplitude and coherency data and to facilitate generation of specific criteria to facilitate differentiation of true faults from false fault signatures during the extraction process. For example, U.S. Pat. No. 5,987,388 (Crawford et al.) discloses an automatic fault detection method. Another approach based on mathematically inserting test planes into a volume of seismic data to approximate dip and azimuth of potential fault surfaces is disclosed in U.S. Pat. No. 6,018,498 (Neff et al.).
Automatic fault detection may work well with extremely good quality data. However, many seismic data volumes do not have the quality required for automatic methods. Therefore, auto-assisted methods where the seismic interpreter guides the computer by inserting partial interpretations generally are more reliable, particularly with data of lesser quality.
U.S. Pat. No. 5,537,320 (Simpson et al.) discloses one example of an auto-assisted method. This method starts with a manually interpreted fault stick line (a piecewise linear line), which is defined by at least two fault nodes, in a vertical slice of a seismic amplitude volume. A “fault stick” represents the intersection of a fault surface and any planar slice of the data volume. The nodes are points in the slice lying along the fault, and are typically identified by a seismic interpreter. The method of Simpson et al. requires several processes that are used to refine and extend the initial fault seed nodes or initial points designated by the user to represent the fault. First, a “snap” process moves the fault seed nodes so as to be located at voxel points at which minimum correlation occurs between seismic amplitudes in either side of the fault nodes. Voxel points are points in space (or in a grid of a three-dimensional volume) with a location (such as (x,y,z) coordinate) and value (typically, grayscale from 0-255) representing seismic amplitude or its discontinuity. The next step is to extend the two end-point fault seed nodes. An end-point fault seed node is a fault point located at the two ends of a fault stick (or fault polyline). The two end-point fault seed nodes are extended in upward and downward directions respectively with a fixed length and the process makes a decision if the two end-nodes can be extended. The final step projects the snapped and extended fault nodes to a next vertical slice. These projected fault nodes serve as new fault seed nodes and the process is repeated. In this three-step process, a quality control threshold value is used to stop extensions in vertical directions and into the next slices.
The Simpson et al. processes do not use the three-dimensional information inherent in the fault surfaces in a seismic amplitude cube. The “snap” process is a two-dimensional operator, meaning that the decision on moving a fault node in one vertical slice is based only on the information obtainable from that vertical slice. Even in a given vertical slice, the movement of one node is not constrained in any way by the location of the other nodes in the same vertical slice. Furthermore, the technique is not able to handle the fault nodes selected in horizontal slices and vertical slices together. Segmentation of the shapes and boundaries of a three-dimensional object based on a two-dimensional operator without sufficient smoothness constraints is known in the art to be very susceptible to noise in voxel values.
In one variation, disclosed in Simpson et al., fault seed nodes in two or more vertical slices are jointly interpolated to generate fault seed nodes for intervening vertical slices. These newly created nodes in each vertical slice are refined by using the “snap” process in each vertical slice.
The result of these prior art hydrocarbon system oriented techniques is the characterization of the object of interest, generally a fault surface, in the three-dimensional data set. The problem of finding and parameterizing shapes and boundaries of an object in two- and three-dimensional images has also been extensively studied in the image analysis and computer vision literature. Promising models that have robustness to noise and the flexibility to represent a broad class of shapes include deformable surface models and their two-dimensional analog, active contours. See, for example, M. Kass, et al. “Snakes: active contour model,” Int. J. Comput. Vision 1, 321-331 (1988). Kass discloses two-dimensional models, but the principles for three-dimensional models are the same. A deformable surface behaves like an elastic sheet. Initially placed close to a boundary of interest, a deformable surface deforms towards the desired boundary under the influence of external forces (attraction toward salient voxel features or anomalies in the data such as high discontinuity) and internal elastic forces (surface smoothness constraints). Variations of deformable models have been successfully utilized in reconstructing boundaries of brain, heart tissue, and blood vessels from medical images. Examples of references disclosing three-dimensional models for medical applications include L. Cohen and I. Cohen, “Finite-element methods for active contour models and balloons for 2-D and 3-D images,” IEEE Trans. PAMI 15, No. 11 (1993); A. Bosnjak, et al., “Segmentation and VRML visualization of left ventricle in echocardiograph images using 3D deformable models and superquadrics,” Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society 3, 1724-1727 (July, 2000); and M. Hernandez-Hoyos et al., “A deformable vessel model with single point initialization for segmentation, quantification and visualization of blood vessels in 3D MRA,” Proceedings of the Third International Conference of Medical Imaging Computing and Computer-Assisted Intervention, 735-745 (October 2000).
Patents have been issued for using two-dimensional deformable models for automatically determining the boundaries of objects in three-dimensional topographic images (U.S. Pat. No. 6,249,594), and using wavelet coefficients and a two-dimensional model for the detection of nodules in biological tissues (U.S. Pat. No. 6,078,680). Deformable models have been applied to geoscience by Apprato et al, who used a traditional two-dimensional deformable model on a sea floor image to detect a fault line. D. Apprato, C. Gout, and S. Vieira-Teste, “Segmentation of complex geophysical 3D structures,” Proceedings of IEEE 2000 National Geoscience and Remote Sensing Symposium, p. 651-653, July, 2000. Apprato et al uses a standard two-dimensional deformable model for fault segmentation, but on a two-dimensional sea floor image. The model is not suitable for a three-dimensional fault surface segmentation because the model does not utilize seismic volume data that is required for three-dimensional fault surface construction.
Due to the above-mentioned difficulties with traditional automatic interpretations, faults are typically interpreted manually using both amplitude and discontinuity data. The interpreter scrolls through the vertical section of the seismic amplitude cube and digitizes the fault sticks. The fault interpretation is simultaneously co-referenced on time slices of the discontinuity cube for verification that the interpretation satisfies the fault trace on the discontinuity time slice. Depending on the level of accuracy required and the complexity of the fault network, the operator or interpreter may increase the number of fault sticks necessary to describe the fault. Finally, the interpreted fault is gridded or triangulated to create a fault surface using commercially available software (i.e., Gocad™ or Voxelgeo™). The problem with existing methods is that traditional manual interpretation methods require time consuming dense fault sticks for an accurate fault surface and automatic interpretation methods lack accuracy in three-dimensions. Accordingly, there is a need for a rapid, accurate, fault interpretation method. The present invention satisfies that need.